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"""
Helper script to compute BPD of images in a directory.
The script will never modify any files.
All conversions are done in memory on a copy.

Usage: python compute_bpd.py your_glob_pattern [extension] [colorspace] [psnr-check]
Args:
    - extension: any valid PIL image extension (e.g., PNG, WebP, JPEG).
    - colorspace: any valid PIL colorspace plus the lossless YCoCg.
    - psnr-check (flag): if set, will compute PSNR of saved image with respect ot the original colorspace.

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''' 
Usage: python patchify_padded.py 'my_dir/*.png' output_dir 32

This script:
    1) Loads all images matching the glob 'my_dir/*.png'
    2) Padds them with zeros such that the height and width are divisible by the specified patch size (in this case, 32)
    3) Saves them to disk in parallel to the output directory (output_dir)
'''

from PIL import Image

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def push(state, symbol, cdf_func, prec):
    cdf_low, cdf_high = cdf_func(symbol)
    freq = cdf_high - cdf_low
    return prec*(state // freq) + (state % freq) + cdf_low

def pop(state, icdf_func, cdf_func, prec):
    cdf_value = state % prec
    symbol, cdf_low, cdf_high = icdf_func(cdf_value)
    freq = cdf_high - cdf_low
    return symbol, freq*(state // prec) + cdf_value - cdf_low

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import numpy as np

def encode(v):
    changes = np.flatnonzero(np.diff(v)) + 1
    values = np.r_[v[0], v[changes]]
    runlengths = np.diff(np.r_[0, changes, len(v)])
    return runlengths, values

def decode(runlengths, values):
    return np.repeat(values, runlengths)

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# We can produce samples from an un-normalized distribution by adding 
# iid Gumbel noise together with the argmax operator; which is denoted as the Gumbel-Max Trick.
# However, argmax doesn't produce meaningful gradient signals, so we replace argmax
# by softmax, with a temperature parameter (Gumbel-Softmax Trick).
#
# I didn't invent any of this, all credit goes to the following papers:
# - https://arxiv.org/abs/1611.00712
# - https://arxiv.org/abs/1611.01144

import numpy as np

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from time import time
from contextlib import contextmanager
import json
import torch
import logging

logging.basicConfig(stream=sys.stdout,
    level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s:%(message)s')
logger = logging.getLogger()

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'''
This file is heavily inspired by https://github.com/j-towns/ans-notes/blob/master/rans.py

We describe a variant of bits-back coding called BB-Huffman. This file is meant
purely as an educational tool and is in no way optimized. The goals are to

1. illustrate how bits-back coding can be used with Huffman-like codecs;
2. and how the cost of using a selector to toggle between codebooks can be greatly reduced.

- Symbols are integers between 0 and 2;

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call plug#begin()
  Plug 'lervag/vimtex'
  Plug 'manicmaniac/coconut.vim'
  Plug 'goerz/jupytext.vim'
  Plug 'skanehira/preview-markdown.vim'
call plug#end()

tnoremap <Esc> <C-\><C-n>
tnoremap <C-A> pwd\|xclip -selection clipboard<CR><C-\><C-n>:cd <C-r>+<CR>i
vnoremap <C-c> "+y

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import pandas as pd

from hashlib import sha256
from pandas.util import hash_pandas_object
from functools import lru_cache


class HashableDataFrame(pd.DataFrame):
    def __init__(self, obj):
        super().__init__(obj)